Application of multi-label classification models for the diagnosis of diabetic complications.

ABSTRACT:

Background

Early diagnosis for the diabetes complications is clinically demanding with great significancy. Regarding the complexity of diabetes complications, we applied a multi-label classification (MLC) model to predict four diabetic complications simultaneously using data in the modern electronic health records (EHRs), and leveraged the correlations between the complications to further improve the prediction accuracy.

Methods

We obtained the demographic characteristics and laboratory data from the EHRs for patients admitted to Changzhou No. 2 People's Hospital, the affiliated hospital of Nanjing Medical University in China from May 2013 to June 2020. The data included 93 biochemical indicators and 9,765 patients. We used the Pearson correlation coefficient (PCC) to analyz

SUBMITTER: Zhou L 

PROVIDER: S-EPMC8182940 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature